[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71NMZTPJ090TFPMXWA1XVFZ":3,"skill-same:01M71NMZTPJ090TFPMXWA1XVFZ":102},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":14,"repositoryHtmlUrl":50,"repositoryHomepageUrl":51,"ownerHtmlUrl":52,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":53,"install":54,"resources":67},"01M71NMZTPJ090TFPMXWA1XVFZ","jetson-package",null,"Pick Jetson-compatible containers, vLLM runtime images, and Jetson AI Lab PyPI indexes; maps Orin SM 8.7 vs Thor SM 11.0 and JetPack-specific package choices.","pattanation\u002FNorvik","pattanation","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235419268?v=4","01M71M5THHYYB15ME8R2E4FCZG","Patta","01M71M5TJM1NDM992M8PDKYR2K",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fjetson-package\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34],"package","jetson package","images","runtime","containers","thor","pick","lab","pypi","vLLM","Bash","team","jetpack","maps","indexes","public","2026-11-10 10:56:23",false,"---\nname: jetson-package\ndescription: Pick Jetson-compatible containers, vLLM runtime images, and Jetson AI Lab PyPI indexes; maps Orin SM 8.7 vs Thor SM 11.0 and JetPack-specific package choices.\nversion: 0.0.1\nlicense: \"Apache-2.0\"\nmetadata:\n  author: \"Jetson Team\"\n  tags: [jetson, package, containers]\n  languages: [bash]\n  data-classification: public\n---\n\n# Jetson Package & Environment\n\nAgents often suggest `docker pull` images or `pip install` wheels that claim **aarch64** support but were never built for Jetson’s GPU **streaming multiprocessor (SM)** targets. On Jetson, **default to NVIDIA-curated artifacts** unless the user explicitly opts out.\n\n## Purpose\n\nChoose Jetson-compatible containers and Python package indexes before installing GPU-native ML stacks. This skill prevents agents from recommending generic ARM wheels or stale container tags that do not include the right CUDA, JetPack, or SM target for the device.\n\n## When to use\n\n- \"Which Docker image \u002F container should I use on this Jetson?\"\n- \"Where do I get PyTorch \u002F vLLM \u002F CUDA wheels for Jetson?\"\n- \"`pip install` failed\" or \"wrong CUDA \u002F SM\" after installing a generic ARM wheel.\n- Before `docker run` or `pip install` for ML stacks on Orin or Thor.\n- User or agent looks for `l4t-cuda` containers on NGC — redirect to `nvcr.io\u002Fnvidia\u002Fcuda` (multi-arch).\n- \"Which PyTorch container should I use on Jetson?\" — answer depends on Thor vs Orin and JetPack version.\n\n## Canonical sources (use these first)\n\n1. **Prebuilt containers (GHCR)** — [NVIDIA-AI-IOT packages](https:\u002F\u002Fgithub.com\u002Forgs\u002FNVIDIA-AI-IOT\u002Fpackages): `llama_cpp`, `ollama`, `live-vlm-webui`, older-Orin `vllm`, and related images built for Jetson JetPack stacks. Prefer these over random `arm64` images on Docker Hub. For vLLM, use upstream `vllm\u002Fvllm-openai` on Thor and Orin JetPack 7.2 \u002F L4T r39+.\n2. **NGC CUDA \u002F PyTorch containers** — Tag selection depends on Jetson generation. Do not treat example PyTorch tag shapes as pinned recommendations; look up the current tag in the [NGC PyTorch catalog](https:\u002F\u002Fcatalog.ngc.nvidia.com\u002Forgs\u002Fnvidia\u002Fcontainers\u002Fpytorch) before giving a command.\n\n   | Jetson | CUDA base | PyTorch |\n   |--------|-----------|---------|\n   | **Thor** | `nvcr.io\u002Fnvidia\u002Fcuda:\u003Cver>-devel-ubuntu\u003Cver>` (multi-arch, arm64 included) | `nvcr.io\u002Fnvidia\u002Fpytorch:\u003Ccurrent-tag>-py3` (main multi-arch tag; verify current NGC tag) |\n   | **Orin + r36 \u002F JetPack 6** | same multi-arch CUDA base | `nvcr.io\u002Fnvidia\u002Fpytorch:\u003Ccurrent-tag>-py3-igpu` — verify the current NGC tag and use the `-igpu` suffix for Orin iGPU (SM 8.7) when NGC publishes it |\n   | **Orin + r39+ (future)** | same | likely main multi-arch tag once Orin becomes SBSA; verify when r39 ships |\n\n**`l4t-cuda` is the legacy Orin-era CUDA container line.** If a user cannot find `l4t-cuda` on NGC, redirect them to the current multi-arch `nvcr.io\u002Fnvidia\u002Fcuda` image instead of third-party images.\n3. **Python package indexes (devpi)** — [Jetson AI Lab PyPI](https:\u002F\u002Fpypi.jetson-ai-lab.io\u002F): browse the tree (for example `jp6\u002Fcu126`, `jp6\u002Fcu128`) and pick the index that matches your **JetPack \u002F CUDA userland**. Prefer these over PyPI-only wheels for GPU-native stacks.\n\n## GPU architecture reminder (why generic ARM fails)\n\n| Jetson family | CUDA compute capability | Build target | Note |\n|---------------|-------------------------|--------------|------|\n| Orin (AGX \u002F NX \u002F Nano) | **8.7** | `sm_87` | Many desktop `aarch64` wheels omit Jetson Orin kernels. |\n| Thor (T5000 \u002F T4000) | **11.0** | `sm_110` | Requires CUDA \u002F wheels \u002F containers that include Blackwell Jetson support. |\n\nA wheel or container may install on **ARM64 Linux** and still be **unusable or slow** if CUDA kernels were not compiled for your Jetson’s SM.\n\nUse CUDA build target names when discussing wheel compatibility: `sm_87` for Jetson Orin and `sm_110` for Jetson Thor. Do not infer the generation from a prompt or a hostname — run `scripts\u002Fartifact_hints.sh` and use its detected `generation`, `variant`, `l4t`, and `cuda_sm_hint` fields before recommending wheels or container tags.\n\n## GPU Python wheels on Jetson\n\nDefault PyPI wheels for GPU-native packages are usually not the right answer on Jetson, even when they claim `aarch64` support. For `onnxruntime-gpu`, PyTorch, vLLM, and similar packages, use the Jetson AI Lab package index as the canonical source and choose the subtree that matches the device's JetPack \u002F CUDA userland.\n\nFor `onnxruntime-gpu`, lead with Jetson AI Lab rather than plain PyPI:\n\n```bash\npip install --extra-index-url https:\u002F\u002Fpypi.jetson-ai-lab.io\u002Fjp6\u002Fcu126\u002F+simple\u002F onnxruntime-gpu\n```\n\nAdjust the `jp6\u002Fcu126` portion to match the detected JetPack \u002F CUDA line. Do not present `pip install onnxruntime-gpu` from default PyPI as an equivalent Jetson GPU option.\n\n## Do not fabricate device facts\n\nDo not invent SKU names, RAM sizes, JetPack versions, CUDA versions, or GPU SM targets. Quote only what `scripts\u002Fartifact_hints.sh` or the user's supplied environment reports. If a field is unavailable, omit it or say it is unknown.\n\n## Prerequisites\n\n- Run package-detection scripts on a Jetson target, not on the host workstation.\n- Network access is needed to inspect GHCR, NGC, or Jetson AI Lab package indexes.\n- Source device facts from `scripts\u002Fartifact_hints.sh`, `jetson-diagnostic`, or user-provided environment output before recommending tags or wheels.\n\n## Available Scripts\n\n| Script | Purpose | Arguments |\n|--------|---------|-----------|\n| `scripts\u002Fartifact_hints.sh` | Emits detected Jetson SKU\u002Fgeneration, CUDA SM hint, canonical package URLs, and a preferred vLLM image hint. | `--human` for a readable summary; no argument for JSON. |\n\nIf your agent runtime supports `run_script`, use it to run `scripts\u002Fartifact_hints.sh` and read the JSON output. Otherwise run the script with `bash` from the repository root.\n\n## Instructions\n\n1. Run `scripts\u002Fartifact_hints.sh` (JSON on stdout). It sources `skills\u002Fjetson-diagnostic\u002Fscripts\u002Fdetect_jetson.sh` and returns `sku`, `generation`, `product_line`, `variant`, `l4t`, a preferred **vLLM** image, `cuda_sm_hint`, and canonical URLs.\n2. For **pip**, open the devpi root in a browser, pick the **jp6** subtree that matches your CUDA line, and set `--extra-index-url` \u002F `PIP_EXTRA_INDEX_URL` — see `references\u002Fpypi-jetson-ai-lab.md`.\n3. For **containers**, see `references\u002Fghcr-images.md` and `jetson-llm-serve` for vLLM.\n\n## Limitations\n\n- This skill points to package catalogs and emits compatibility hints; it does not verify that a specific model checkpoint fits in memory.\n- NGC and GHCR tags change. Treat placeholder tag shapes such as `\u003Ccurrent-tag>-py3` as lookup instructions, not literal tags.\n- If `generation` or `cuda_sm_hint` is unknown, do not guess a container tag.\n\n## Hand off to\n\n- `jetson-llm-serve` — run upstream\u002Fnative vLLM 0.20+ on Thor and Orin JetPack 7.2 \u002F L4T r39+, or `vllm:latest-jetson-orin` on older Orin.\n- `jetson-llm-benchmark` — measure after the stack is installed.\n- `jetson-diagnostic` — if installs succeed but runtime fails, snapshot first.\n\n## Safety\n\nRead-only: points to catalogs and emits hints; does not install or pull.\n\n## Sources\n\n[NVIDIA-AI-IOT GitHub Packages](https:\u002F\u002Fgithub.com\u002Forgs\u002FNVIDIA-AI-IOT\u002Fpackages), [pypi.jetson-ai-lab.io](https:\u002F\u002Fpypi.jetson-ai-lab.io\u002F).\n","# Jetson Package & Environment\n\nAgents often suggest `docker pull` images or `pip install` wheels that claim **aarch64** support but were never built for Jetson’s GPU **streaming multiprocessor (SM)** targets. On Jetson, **default to NVIDIA-curated artifacts** unless the user explicitly opts out.\n\n## Purpose\n\nChoose Jetson-compatible containers and Python package indexes before installing GPU-native ML stacks. This skill prevents agents from recommending generic ARM wheels or stale container tags that do not include the right CUDA, JetPack, or SM target for the device.\n\n## When to use\n\n- \"Which Docker image \u002F container should I use on this Jetson?\"\n- \"Where do I get PyTorch \u002F vLLM \u002F CUDA wheels for Jetson?\"\n- \"`pip install` failed\" or \"wrong CUDA \u002F SM\" after installing a generic ARM wheel.\n- Before `docker run` or `pip install` for ML stacks on Orin or Thor.\n- User or agent looks for `l4t-cuda` containers on NGC — redirect to `nvcr.io\u002Fnvidia\u002Fcuda` (multi-arch).\n- \"Which PyTorch container should I use on Jetson?\" — answer depends on Thor vs Orin and JetPack version.\n\n## Canonical sources (use these first)\n\n1. **Prebuilt containers (GHCR)** — [NVIDIA-AI-IOT packages](https:\u002F\u002Fgithub.com\u002Forgs\u002FNVIDIA-AI-IOT\u002Fpackages): `llama_cpp`, `ollama`, `live-vlm-webui`, older-Orin `vllm`, and related images built for Jetson JetPack stacks. Prefer these over random `arm64` images on Docker Hub. For vLLM, use upstream `vllm\u002Fvllm-openai` on Thor and Orin JetPack 7.2 \u002F L4T r39+.\n2. **NGC CUDA \u002F PyTorch containers** — Tag selection depends on Jetson generation. Do not treat example PyTorch tag shapes as pinned recommendations; look up the current tag in the [NGC PyTorch catalog](https:\u002F\u002Fcatalog.ngc.nvidia.com\u002Forgs\u002Fnvidia\u002Fcontainers\u002Fpytorch) before giving a command.\n\n   | Jetson | CUDA base | PyTorch |\n   |--------|-----------|---------|\n   | **Thor** | `nvcr.io\u002Fnvidia\u002Fcuda:\u003Cver>-devel-ubuntu\u003Cver>` (multi-arch, arm64 included) | `nvcr.io\u002Fnvidia\u002Fpytorch:\u003Ccurrent-tag>-py3` (main multi-arch tag; verify current NGC tag) |\n   | **Orin + r36 \u002F JetPack 6** | same multi-arch CUDA base | `nvcr.io\u002Fnvidia\u002Fpytorch:\u003Ccurrent-tag>-py3-igpu` — verify the current NGC tag and use the `-igpu` suffix for Orin iGPU (SM 8.7) when NGC publishes it |\n   | **Orin + r39+ (future)** | same | likely main multi-arch tag once Orin becomes SBSA; verify when r39 ships |\n\n**`l4t-cuda` is the legacy Orin-era CUDA container line.** If a user cannot find `l4t-cuda` on NGC, redirect them to the current multi-arch `nvcr.io\u002Fnvidia\u002Fcuda` image instead of third-party images.\n3. **Python package indexes (devpi)** — [Jetson AI Lab PyPI](https:\u002F\u002Fpypi.jetson-ai-lab.io\u002F): browse the tree (for example `jp6\u002Fcu126`, `jp6\u002Fcu128`) and pick the index that matches your **JetPack \u002F CUDA userland**. Prefer these over PyPI-only wheels for GPU-native stacks.\n\n## GPU architecture reminder (why generic ARM fails)\n\n| Jetson family | CUDA compute capability | Build target | Note |\n|---------------|-------------------------|--------------|------|\n| Orin (AGX \u002F NX \u002F Nano) | **8.7** | `sm_87` | Many desktop `aarch64` wheels omit Jetson Orin kernels. |\n| Thor (T5000 \u002F T4000) | **11.0** | `sm_110` | Requires CUDA \u002F wheels \u002F containers that include Blackwell Jetson support. |\n\nA wheel or container may install on **ARM64 Linux** and still be **unusable or slow** if CUDA kernels were not compiled for your Jetson’s SM.\n\nUse CUDA build target names when discussing wheel compatibility: `sm_87` for Jetson Orin and `sm_110` for Jetson Thor. Do not infer the generation from a prompt or a hostname — run `scripts\u002Fartifact_hints.sh` and use its detected `generation`, `variant`, `l4t`, and `cuda_sm_hint` fields before recommending wheels or container tags.\n\n## GPU Python wheels on Jetson\n\nDefault PyPI wheels for GPU-native packages are usually not the right answer on Jetson, even when they claim `aarch64` support. For `onnxruntime-gpu`, PyTorch, vLLM, and similar packages, use the Jetson AI Lab package index as the canonical source and choose the subtree that matches the device's JetPack \u002F CUDA userland.\n\nFor `onnxruntime-gpu`, lead with Jetson AI Lab rather than plain PyPI:\n\n```bash\npip install --extra-index-url https:\u002F\u002Fpypi.jetson-ai-lab.io\u002Fjp6\u002Fcu126\u002F+simple\u002F onnxruntime-gpu\n```\n\nAdjust the `jp6\u002Fcu126` portion to match the detected JetPack \u002F CUDA line. Do not present `pip install onnxruntime-gpu` from default PyPI as an equivalent Jetson GPU option.\n\n## Do not fabricate device facts\n\nDo not invent SKU names, RAM sizes, JetPack versions, CUDA versions, or GPU SM targets. Quote only what `scripts\u002Fartifact_hints.sh` or the user's supplied environment reports. If a field is unavailable, omit it or say it is unknown.\n\n## Prerequisites\n\n- Run package-detection scripts on a Jetson target, not on the host workstation.\n- Network access is needed to inspect GHCR, NGC, or Jetson AI Lab package indexes.\n- Source device facts from `scripts\u002Fartifact_hints.sh`, `jetson-diagnostic`, or user-provided environment output before recommending tags or wheels.\n\n## Available Scripts\n\n| Script | Purpose | Arguments |\n|--------|---------|-----------|\n| `scripts\u002Fartifact_hints.sh` | Emits detected Jetson SKU\u002Fgeneration, CUDA SM hint, canonical package URLs, and a preferred vLLM image hint. | `--human` for a readable summary; no argument for JSON. |\n\nIf your agent runtime supports `run_script`, use it to run `scripts\u002Fartifact_hints.sh` and read the JSON output. Otherwise run the script with `bash` from the repository root.\n\n## Instructions\n\n1. Run `scripts\u002Fartifact_hints.sh` (JSON on stdout). It sources `skills\u002Fjetson-diagnostic\u002Fscripts\u002Fdetect_jetson.sh` and returns `sku`, `generation`, `product_line`, `variant`, `l4t`, a preferred **vLLM** image, `cuda_sm_hint`, and canonical URLs.\n2. For **pip**, open the devpi root in a browser, pick the **jp6** subtree that matches your CUDA line, and set `--extra-index-url` \u002F `PIP_EXTRA_INDEX_URL` — see `references\u002Fpypi-jetson-ai-lab.md`.\n3. For **containers**, see `references\u002Fghcr-images.md` and `jetson-llm-serve` for vLLM.\n\n## Limitations\n\n- This skill points to package catalogs and emits compatibility hints; it does not verify that a specific model checkpoint fits in memory.\n- NGC and GHCR tags change. Treat placeholder tag shapes such as `\u003Ccurrent-tag>-py3` as lookup instructions, not literal tags.\n- If `generation` or `cuda_sm_hint` is unknown, do not guess a container tag.\n\n## Hand off to\n\n- `jetson-llm-serve` — run upstream\u002Fnative vLLM 0.20+ on Thor and Orin JetPack 7.2 \u002F L4T r39+, or `vllm:latest-jetson-orin` on older Orin.\n- `jetson-llm-benchmark` — measure after the stack is installed.\n- `jetson-diagnostic` — if installs succeed but runtime fails, snapshot first.\n\n## Safety\n\nRead-only: points to catalogs and emits hints; does not install or pull.\n\n## Sources\n\n[NVIDIA-AI-IOT GitHub Packages](https:\u002F\u002Fgithub.com\u002Forgs\u002FNVIDIA-AI-IOT\u002Fpackages), [pypi.jetson-ai-lab.io](https:\u002F\u002Fpypi.jetson-ai-lab.io\u002F).\n",{"name":5,"license":15,"version":40,"metadata":41,"description":7},"0.0.1",{"tags":42,"author":44,"languages":45,"data-classification":34},[43,19,23],"jetson","Jetson Team",[46],"bash","claude\u002Finstall-gsd-build-foQdV","d86cd127f527124b5fd0fd0bc679a4d79b7d01d7",".agents\u002Fskills\u002Fjetson-package","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik","https:\u002F\u002Fnorvik-orpin.vercel.app","https:\u002F\u002Fgithub.com\u002Fpattanation","2026-07-17 11:17:04",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add pattanation\u002FNorvik --skill jetson-package",{"tool":60,"command":61},"bunx","bunx skills add pattanation\u002FNorvik --skill jetson-package",{"tool":63,"command":64},"pnpm","pnpm dlx skills add pattanation\u002FNorvik --skill jetson-package","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fjetson-package\u002FSKILL.md",true,[68,74,78,82,86,92,96],{"path":69,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"BENCHMARK.md","REFERENCE","参考文档",3302,"https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fjetson-package\u002FBENCHMARK.md",{"path":75,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":76,"sourceHtmlUrl":77},"evals\u002Fevals.json",5402,"https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fjetson-package\u002Fevals\u002Fevals.json",{"path":79,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Fghcr-images.md",1027,"https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fjetson-package\u002Freferences\u002Fghcr-images.md",{"path":83,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fpypi-jetson-ai-lab.md",943,"https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fjetson-package\u002Freferences\u002Fpypi-jetson-ai-lab.md",{"path":87,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":90,"sourceHtmlUrl":91},"scripts\u002Fartifact_hints.sh","SCRIPT","脚本",3392,"https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fjetson-package\u002Fscripts\u002Fartifact_hints.sh",{"path":93,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":94,"sourceHtmlUrl":95},"skill-card.md",3973,"https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fjetson-package\u002Fskill-card.md",{"path":97,"resourceType":98,"resourceTypeDesc":99,"sizeBytes":100,"sourceHtmlUrl":101},"skill.oms.sig","OTHER","其他",5309,"https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fjetson-package\u002Fskill.oms.sig",[103,137,163,187,209,224],{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":107,"sourceHtmlUrl":108,"categories":109,"keywords":118,"publishedAt":136},"01M71QDV4DM8T19HEHWATEF1K4","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. 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